Diagnostics data recorded for the purposes of failure diagnosis in complex systems are often very extensive, leading to large file sizes and download times. While this extensive diagnostics data is a necessity of supplying maximal first failure data capture, it is at the expense of increased data volume, which often contains high quantities of redundant data.
When downloading large diagnostics bundles over, for example, a restricted bandwidth network, download times can become very large even though the amount of data in the bundle that is actually relevant and helpful to failure diagnosis is likely to be proportionally small.
An approach to this problem includes breaking down diagnostics into smaller, possibly feature-centric, bundles. This approach requires a well architected organization of diagnostic data and usually some manual process to decide the full set of bundles required to diagnose a failure. Further, any addition of product components, and thus diagnostics, results in reworking bundle content. Also, bundles are likely to contain a potentially large amount of data that is not helpful to diagnosing the failure.
Another approach includes reducing the amount of diagnostics data that is produced. Yet, components with reduced diagnostics may still fail, while failing to produce enough trace to diagnose the problem.